Systems and methods for user interface adaptation for per-user metrics

ABSTRACT

A computer system for transforming a user interface according to data store mining includes a data store configured to store a parameter related to a user and index event data of a set of events. A data processing circuit is configured to identify a first set of identifiers and train a machine learning model based on event data by the data store. An interface circuit is configured to receive an indication of a selected identifier of the plurality of identifiers, determine a first intake metric of the selected identifier using the machine learning model, and a second intake metric of the selected identifier and the parameter using the machine learning model. The interface circuit is configured to transform the user interface according to the first intake metric and the second intake metric.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 17/121,943, filed Dec. 15, 2020, which is a continuation ofU.S. patent application Ser. No. 16/117,140, filed Aug. 30, 2018 (nowU.S. Pat. No. 10,896,048). The entire disclosures of the aboveapplications are incorporated by reference.

FIELD

The present disclosure relates to user interface adaptation and, moreparticularly, to determining a per-user metric to transform the userinterface.

BACKGROUND

Currently, entities, such as high-volume pharmacies, offer online drugmanagement programs. For example, a user who is a member of a pharmacycan create an account on a user device via a web portal to access thedrug management program. Each user may be able to access the sameinformation and may be presented with an identical user interface.Similarly, a support representative or an analyst working for thepharmacy may access user information that is not customized to the useror the user population.

The background description provided here is for the purpose of generallypresenting the context of the disclosure. Work of the presently namedinventors, to the extent it is described in this background section, aswell as aspects of the description that may not otherwise qualify asprior art at the time of filing, are neither expressly nor impliedlyadmitted as prior art against the present disclosure.

SUMMARY

A computer system for transforming a user interface according to datastore mining is presented. The computer system may include a data storeconfigured to store a parameter related to a user and to index eventdata of a plurality of events. Each event of the plurality of events maycorrespond to a physical object being supplied to the user identified byan identifier on behalf of an entity. The data store may be configuredto store descriptive data for each of a plurality of identifiers.

The system may include a data processing circuit configured to identifya first set of identifiers from the plurality of identifiers based oncommonality among the descriptive data stored by the data store acrossthe first set of identifiers. The system may also include a dataprocessing circuit. The data processing circuit may be configured toidentify a first set of identifiers from the plurality of identifiersbased on commonality among the descriptive data stored by the data storeacross the first set of identifiers. The data processing circuit may beconfigured to train a machine learning model for the first set ofidentifiers based on event data stored by the data store for the firstset of identifiers from within a predetermined epoch. The machinelearning model may be trained using parallel processing of records fromthe data store. The parallel processing may include assigning analysisof the indexed event data of each of a subset of the first set ofidentifiers to respective processor threads for parallel execution onprocessing hardware.

The system may further include an interface circuit. The interfacecircuit may be configured to receive an indication of a selectedidentifier of the plurality of identifiers. The interface circuit may beconfigured to determine a first intake metric of the selected identifierusing the machine learning model from the data processing circuit. Theinterface circuit may also be configured to determine a second intakemetric of the selected identifier and the parameter using the machinelearning model from the data processing circuit. The interface circuitmay be configured to transform the user interface according to the firstintake metric and the second intake metric. The first intake metric mayrepresent an amount of resources expected to be received by the entityfrom the selected identifier during a second epoch subsequent to thepredetermined epoch. The second intake metric may represent an amount ofresources expected to be received by the entity from the selectedidentifier and the parameter during the second epoch.

In other features, the system may include a data analyst deviceconfigured to render the user interface and transmit a message includingthe indication of the selected identifier. The data analyst device maybe configured to determine whether the first intake metric is greaterthan the second intake metric. The data analyst device may be configuredto calculate a difference between the first intake metric and the secondintake metric in response to the data analyst device determining thatthe first intake metric is greater than the second intake metric. Inother features, the data analyst device may be configured to render atleast one of the first intake metric, the second intake metric, and thecalculated difference between the first intake metric and the secondintake metric on the user interface in response to the data analystdevice determining that the first intake metric is greater than thesecond intake metric. In other features, the data analyst device may beconfigured to render the second intake metric on the user interface inresponse to the data analyst device determining that the first intakemetric is not greater than the second intake metric.

In other features, the data processing circuit may be configured toidentify a selected identifier from the first set of identifiers andtrain the machine learning model to predict an expected increase in theselected identifier during the second epoch. The machine learning modelmay be trained based on event data store by the data store for the firstset of identifiers from within the predetermined epoch. In otherfeatures, the interface circuit may be configured to determine the firstintake metric for the selected identifier further based on at least oneof: a retention value, a population retention value, and the expectedincrease calculated by the data processing circuit. The retention valuemay indicate a likelihood of the selected identifier being associatedwith the entity for the second epoch. The population retention value mayindicate a likelihood of a population of identifiers encompassing theselected identifier being associated with the entity for the secondepoch.

In other features, the interface circuit may be configured to determinethe second intake metric for the selected identifier based on aretention value, a population retention value, and the expected increasein the selected identifier calculated by the data processing circuit.The retention value may indicate a likelihood of the selected identifierbeing associated with the entity for the second epoch. The populationretention value may indicate a likelihood of a population of identifiersencompassing the selected identifier being associated with the entityfor the second epoch.

A method for transforming a user interface according to data storemining is presented. The method may include storing, at a data store, aparameter related to a user, and indexing, at the data store, aplurality of events. Each event of the plurality of events maycorrespond to a physical object being supplied to a user identified byan identifier on behalf of an entity. The data store may be configuredto store descriptive data for each of a plurality of identifiers. Themethod may also include identifying, at a data processing circuit, afirst set of identifiers from the plurality of identifiers based oncommonality among the descriptive data stored by the data store acrossthe first set of identifiers. The method may include training, at thedata processing circuit, a machine learning model for the first set ofidentifiers based on event data stored by the data store for the firstset of identifiers from within a predetermined epoch.

The method may also include receiving, at an interface circuit, anindication of a selected identifier of the plurality of identifiers. Themethod may also include determining, at the interface circuit, a firstintake metric of the selected identifiers using the machine learningmodel from the data processing circuit. The method may includedetermining, at the interface circuit, a second intake metric of theselected identifier and the parameter using the machine learning modelfrom the data processing circuit. The method may also includetransforming the user interface according to the first intake metric andthe second intake metric. The machine learning model may be trainedusing parallel processing of records from the data store. The parallelprocessing may include assigning analysis of the indexed event data ofeach of a subset of the first set of identifiers to respective processorthreads for parallel execution on processing hardware. The first intakemetric may represent an amount of resources expected to be received bythe entity from the selected identifier during a second epoch subsequentto the predetermined epoch. The second intake metric may represent anamount of resources expected to be received by the entity from theselected identifier and the parameter during the second epoch.

In other features, the method may include rendering, at a data analystdevice, the user interface and transmitting a message that identifies aselected identifier of the plurality of identifiers. In other features,the method may include determining, at the data analyst, that the firstintake metric is greater than the second intake metric and calculating adifference between the first intake metric and the second intake metric.In other features, the method may include rendering, at the data analystdevice, the first intake metric on the user interface. In otherfeatures, the method may include rendering, at the data analyst device,the second intake metric on the user interface. In other features, themethod may include rendering, at the data analyst device, the calculateddifference between the first intake metric and the second intake metric.In other features, the method may also include determining, at the dataanalyst device, that the first intake metric is not greater than thesecond intake metric and rendering the second intake metric on the userinterface.

In other features, the method may include identifying, at the dataprocessing circuit, a selected identifier from the first set ofidentifiers and training, at the data processing circuit, the machinelearning model to predict an expected increase in the selectedidentifier during the second epoch. In other features, the machinelearning model may be trained based on event data stored by the datastore for the first set of identifiers from within the predeterminedepoch. In other features, the first intake metric may be determinedfurther based on the expected increase in the selected identifiercalculated by the data processing circuit.

In other features, the first intake metric may be determined furtherbased on a retention value indicating a likelihood of the selectedidentifier being associated with the entity for the second epoch and apopulation retention value indicating a likelihood of a population ofidentifiers encompassing the selected identifier being associated withthe entity for the second epoch. In other features, the second intakemetric may be determined further based on a retention value indicating alikelihood of the selected identifier being associated with the entityfor the second epoch, a population retention value indicating alikelihood of a population of identifiers encompassing the selectedidentifier being associated with the entity for the second epoch, andthe expected increase in the selected identifier calculated by the dataprocessing circuit.

A non-transitory computer-readable medium with executable instructionsfor performing steps in a method for transforming a user interfaceaccording to data store mining is also presented. The executableinstructions may configure a controller to store, at a data store, aparameter related to a user. The executable instructions may alsoconfigure the controller to index, at the data store, a plurality ofevents. Each event of the plurality of events may correspond to aphysical object being supplied to a user identified by an identifier onbehalf of an entity. The data store may be configured to storedescriptive data for each of a plurality of identifiers. The executableinstructions may also configure the controller to identify, at a dataprocessing circuit, a first set of identifiers from the plurality ofidentifiers based on commonality among the descriptive data stored bythe data store across the first set of identifiers.

The executable instructions may also configure the controller to train,at the data processing circuit, a machine learning model for the firstset of identifiers based on event data stored by the data store for thefirst set of identifiers from within a predetermined epoch. Theexecutable instructions may also configure the controller to receive, atan interface circuit, an indication of a selected identifier of theplurality of identifiers. The executable instructions may also configurethe controller to determine, at the interface circuit, a first intakemetric of the selected identifiers using the machine learning model fromthe data processing circuit. The executable instructions may alsoconfigure the controller to transform the user interface according tothe first intake metric and the second intake metric.

In other features, the machine learning model may be trained usingparallel processing of records from the data store. The parallelprocessing may include assigning analysis of the index event data ofeach of a subset of the first set of identifiers to respective processorthreads for parallel execution on processing hardware. The first intakemetric may represent an amount of resources expected to be received bythe entity from the selected identifier during a second epoch subsequentto the predetermined epoch. The second intake metric may represent anamount of resources expected to be received by the entity from theselected identifier and the parameter during the second epoch.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description, the claims, and the drawings.The detailed description and specific examples are intended for purposesof illustration only and are not intended to limit the scope of thedisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings.

FIG. 1 is a functional block diagram of an example system including ahigh-volume pharmacy.

FIG. 2 is a functional block diagram of an example pharmacy fulfillmentdevice, which may be deployed within the system of FIG. 1.

FIG. 3 is a functional block diagram of an example order processingdevice, which may be deployed within the system of FIG. 1.

FIG. 4 is a functional block diagram of an example per-user metricdetermination device.

FIG. 5 is a flowchart of an example per-user metric being determined.

FIG. 6 is a flowchart of an example per-user metric calculation.

FIG. 7 is a flowchart of an example persona selection for a web portal.

FIG. 8 is a flowchart of an example persona selection for a supportdevice.

FIG. 9A is an example user interface adaptation of a prescriptioninterface for a low per-user metric.

FIG. 9B is an example user interface adaptation of a prescriptioninterface for a high per-user metric.

FIG. 10 is a flowchart of example determinations of a per-user metricrequest.

FIG. 11 is a flowchart of an example per-user metric calculation.

FIG. 12 is a flowchart of an example per-user metric calculation andexample projected per-user metric calculation.

FIG. 13 is a flowchart of an example process for displaying an exampleper-user metric and projected per-user metric on a user interface.

In the drawings, reference numbers may be reused to identify similarand/or identical elements.

DETAILED DESCRIPTION Introduction

Adapting a user interface based on a per-user metric provides apersonalized user interface, including personalized options andbenefits, directly to a user. In various implementations, user interfaceadaptation based on the per-user metric can also be provided to supportrepresentatives and analysts when using their respective devices. Theper-user metric is determined based on data stored in a storage deviceof a pharmacy—for example, member data and claims data stored in thestorage device of the pharmacy.

In various implementations, the per-user metric can be determined basedon an expected user retention, an expected population retention, anexpected output of the user, and an expected intake of the user. Forexample, the expected user retention not only considers the expecteduser retention with the pharmacy, but also considers the expected userretention within the user population—for example, the user's employer.

Additionally, to determine the other features used to determine theper-user metric, a training group of available member claims and claimsdata can be analyzed to determined expected future values. For example,to anticipate the expected population retention, a likelihood ofretention is calculated based on historical retention rates. Similarly,the expected output and intake are calculated based on historicaloutputs and intakes.

Once determined, the per-user metric is used to customize the userinterface displayed to the user, an analyst, and/or a supportrepresentative. For example, based on the per-user metric, acorresponding persona is selected for display to the user at the userdevice. Based on the selected persona, the user interface is transformedto encourage that user's retention and to increase that user's per-usermetric. In various implementations, the expected user retention is anadditional factor considered when transforming the user interface.

For the analyst, the user interface may be transformed according to apersona corresponding to the analyzed user or user population's per-usermetric. Additionally, for analyst purposes, the per-user metric may bedisplayed along with analytics related to the user or user populationbeing analyzed. For the support representative, the user interface maytransform according to the user persona and provide the supportrepresentative with available options to offer the user.

High-Volume Pharmacy

FIG. 1 is a block diagram of an example implementation of a system 100for a high-volume pharmacy. While the system 100 is generally describedas being deployed in a high-volume pharmacy or a fulfillment center (forexample, a mail order pharmacy, a direct delivery pharmacy, etc.), thesystem 100 and/or components of the system 100 may otherwise be deployed(for example, in a lower-volume pharmacy, etc.). A high-volume pharmacymay be a pharmacy that is capable of filling at least some prescriptionsmechanically. The system 100 may include a benefit manager device 102and a pharmacy device 106 in communication with each other directlyand/or over a network 104. The system 100 may also include a storagedevice 110.

The benefit manager device 102 is a device operated by an entity that isat least partially responsible for creation and/or management of thepharmacy or drug benefit. While the entity operating the benefit managerdevice 102 is typically a pharmacy benefit manager (PBM), other entitiesmay operate the benefit manager device 102 on behalf of themselves orother entities (such as PBMs). For example, the benefit manager device102 may be operated by a health plan, a retail pharmacy chain, a drugwholesaler, a data analytics or other type of software-related company,etc. In some implementations, a PBM that provides the pharmacy benefitmay provide one or more additional benefits including a medical orhealth benefit, a dental benefit, a vision benefit, a wellness benefit,a radiology benefit, a pet care benefit, an insurance benefit, a longterm care benefit, a nursing home benefit, etc. The PBM may, in additionto its PBM operations, operate one or more pharmacies. The pharmaciesmay be retail pharmacies, mail order pharmacies, etc.

Some of the operations of the PBM that operates the benefit managerdevice 102 may include the following activities and processes. A member(or a person on behalf of the member) of a pharmacy benefit plan mayobtain a prescription drug at a retail pharmacy location (e.g., alocation of a physical store) from a pharmacist or a pharmacisttechnician. The member may also obtain the prescription drug throughmail order drug delivery from a mail order pharmacy location, such asthe system 100. In some implementations, the member may obtain theprescription drug directly or indirectly through the use of a machine,such as a kiosk, a vending unit, a mobile electronic device, or adifferent type of mechanical device, electrical device, electroniccommunication device, and/or computing device. Such a machine may befilled with the prescription drug in prescription packaging, which mayinclude multiple prescription components, by the system 100. Thepharmacy benefit plan is administered by or through the benefit managerdevice 102.

The member may have a copayment for the prescription drug that reflectsan amount of money that the member is responsible to pay the pharmacyfor the prescription drug. The money paid by the member to the pharmacymay come from, as examples, personal funds of the member, a healthsavings account (HSA) of the member or the member's family, a healthreimbursement arrangement (HRA) of the member or the member's family, ora flexible spending account (FSA) of the member or the member's family.In some instances, an employer of the member may directly or indirectlyfund or reimburse the member for the copayments.

The amount of the copayment required by the member may vary acrossdifferent pharmacy benefit plans having different plan sponsors orclients and/or for different prescription drugs. The member's copaymentmay be a flat copayment (in one example, $10), coinsurance (in oneexample, 10%), and/or a deductible (for example, responsibility for thefirst $500 of annual prescription drug expense, etc.) for certainprescription drugs, certain types and/or classes of prescription drugs,and/or all prescription drugs. The copayment may be stored in thestorage device 110 or determined by the benefit manager device 102.

In some instances, the member may not pay the copayment or may only paya portion of the copayment for the prescription drug. For example, if ausual and customary cost for a generic version of a prescription drug is$4, and the member's flat copayment is $20 for the prescription drug,the member may only need to pay $4 to receive the prescription drug. Inanother example involving a worker's compensation claim, no copaymentmay be due by the member for the prescription drug.

In addition, copayments may also vary based on different deliverychannels for the prescription drug. For example, the copayment forreceiving the prescription drug from a mail order pharmacy location maybe less than the copayment for receiving the prescription drug from aretail pharmacy location.

In conjunction with receiving a copayment (if any) from the member anddispensing the prescription drug to the member, the pharmacy submits aclaim to the PBM for the prescription drug. After receiving the claim,the PBM (such as by using the benefit manager device 102) may performcertain adjudication operations including verifying eligibility for themember, identifying/reviewing an applicable formulary for the member todetermine any appropriate copayment, coinsurance, and deductible for theprescription drug, and performing a drug utilization review (DUR) forthe member. Further, the PBM may provide a response to the pharmacy (forexample, the pharmacy system 100) following performance of at least someof the aforementioned operations.

As part of the adjudication, a plan sponsor (or the PBM on behalf of theplan sponsor) ultimately reimburses the pharmacy for filling theprescription drug when the prescription drug was successfullyadjudicated. The aforementioned adjudication operations generally occurbefore the copayment is received and the prescription drug is dispensed.However in some instances, these operations may occur simultaneously,substantially simultaneously, or in a different order. In addition, moreor fewer adjudication operations may be performed as at least part ofthe adjudication process.

The amount of reimbursement paid to the pharmacy by a plan sponsorand/or money paid by the member may be determined at least partiallybased on types of pharmacy networks in which the pharmacy is included.In some implementations, the amount may also be determined based onother factors. For example, if the member pays the pharmacy for theprescription drug without using the prescription or drug benefitprovided by the PBM, the amount of money paid by the member may behigher than when the member uses the prescription or drug benefit. Insome implementations, the amount of money received by the pharmacy fordispensing the prescription drug and for the prescription drug itselfmay be higher than when the member uses the prescription or drugbenefit. Some or all of the foregoing operations may be performed byexecuting instructions stored in the benefit manager device 102 and/oran additional device.

Examples of the network 104 include a Global System for MobileCommunications (GSM) network, a code division multiple access (CDMA)network, 3rd Generation Partnership Project (3GPP), an Internet Protocol(IP) network, a Wireless Application Protocol (WAP) network, or an IEEE802.11 standards network, as well as various combinations of the abovenetworks. The network 104 may include an optical network. The network104 may be a local area network or a global communication network, suchas the Internet. In some implementations, the network 104 may include anetwork dedicated to prescription orders: a prescribing network such asthe electronic prescribing network operated by Surescripts of Arlington,Va.

Moreover, although the system shows a single network 104, multiplenetworks can be used. The multiple networks may communicate in seriesand/or parallel with each other to link the devices 102-110.

The pharmacy device 106 may be a device associated with a retailpharmacy location (e.g., an exclusive pharmacy location, a grocery storewith a retail pharmacy, or a general sales store with a retail pharmacy)or other type of pharmacy location at which a member attempts to obtaina prescription. The pharmacy may use the pharmacy device 106 to submitthe claim to the PBM for adjudication.

Additionally, in some implementations, the pharmacy device 106 mayenable information exchange between the pharmacy and the PBM. Forexample, this may allow the sharing of member information such as drughistory that may allow the pharmacy to better service a member (forexample, by providing more informed therapy consultation and druginteraction information). In some implementations, the benefit managerdevice 102 may track prescription drug fulfillment and/or otherinformation for users that are not members, or have not identifiedthemselves as members, at the time (or in conjunction with the time) inwhich they seek to have a prescription filled at a pharmacy.

The pharmacy device 106 may include a pharmacy fulfillment device 112,an order processing device 114, and a pharmacy management device 116 incommunication with each other directly and/or over the network 104. Theorder processing device 114 may receive information regarding fillingprescriptions and may direct an order component to one or more devicesof the pharmacy fulfillment device 112 at a pharmacy. The pharmacyfulfillment device 112 may fulfill, dispense, aggregate, and/or pack theorder components of the prescription drugs in accordance with one ormore prescription orders directed by the order processing device 114.

In general, the order processing device 114 is a device located withinor otherwise associated with the pharmacy to enable the pharmacyfulfillment device 112 to fulfill a prescription and dispenseprescription drugs. In some implementations, the order processing device114 may be an external order processing device separate from thepharmacy and in communication with other devices located within thepharmacy.

For example, the external order processing device may communicate withan internal pharmacy order processing device and/or other deviceslocated within the system 100. In some implementations, the externalorder processing device may have limited functionality (e.g., asoperated by a user requesting fulfillment of a prescription drug), whilethe internal pharmacy order processing device may have greaterfunctionality (e.g., as operated by a pharmacist).

The order processing device 114 may track the prescription order as itis fulfilled by the pharmacy fulfillment device 112. The prescriptionorder may include one or more prescription drugs to be filled by thepharmacy. The order processing device 114 may make pharmacy routingdecisions and/or order consolidation decisions for the particularprescription order. The pharmacy routing decisions include whatdevice(s) in the pharmacy are responsible for filling or otherwisehandling certain portions of the prescription order. The orderconsolidation decisions include whether portions of one prescriptionorder or multiple prescription orders should be shipped together for auser or a user family. The order processing device 114 may also trackand/or schedule literature or paperwork associated with eachprescription order or multiple prescription orders that are beingshipped together. In some implementations, the order processing device114 may operate in combination with the pharmacy management device 116.

The order processing device 114 may include circuitry, a processor, amemory to store data and instructions, and communication functionality.The order processing device 114 is dedicated to performing processes,methods, and/or instructions described in this application. Other typesof electronic devices may also be used that are specifically configuredto implement the processes, methods, and/or instructions described infurther detail below.

In some implementations, at least some functionality of the orderprocessing device 114 may be included in the pharmacy management device116. The order processing device 114 may be in a client-serverrelationship with the pharmacy management device 116, in a peer-to-peerrelationship with the pharmacy management device 116, or in a differenttype of relationship with the pharmacy management device 116. The orderprocessing device 114 and/or the pharmacy management device 116 maycommunicate directly (for example, such as by using a local storage)and/or through the network 104 (such as by using a cloud storageconfiguration, software as a service, etc.) with the storage device 110.

The storage device 110 may include: non-transitory storage (for example,memory, hard disk, CD-ROM, etc.) in communication with the benefitmanager device 102 and/or the pharmacy device 106 directly and/or overthe network 104. The non-transitory storage may store order data 118,member data 120, claims data 122, drug data 124, prescription data 126,and/or plan sponsor data 128. Further, the system 100 may includeadditional devices, which may communicate with each other directly orover the network 104.

The order data 118 may be related to a prescription order. The orderdata may include type of the prescription drug (for example, drug nameand strength) and quantity of the prescription drug. The order data 118may also include data used for completion of the prescription, such asprescription materials. In general, prescription materials include anelectronic copy of information regarding the prescription drug forinclusion with or otherwise in conjunction with the fulfilledprescription. The prescription materials may include electronicinformation regarding drug interaction warnings, recommended usage,possible side effects, expiration date, date of prescribing, etc. Theorder data 118 may be used by a high-volume fulfillment center tofulfill a pharmacy order.

In some implementations, the order data 118 includes verificationinformation associated with fulfillment of the prescription in thepharmacy. For example, the order data 118 may include videos and/orimages taken of (i) the prescription drug prior to dispensing, duringdispensing, and/or after dispensing, (ii) the prescription container(for example, a prescription container and sealing lid, prescriptionpackaging, etc.) used to contain the prescription drug prior todispensing, during dispensing, and/or after dispensing, (iii) thepackaging and/or packaging materials used to ship or otherwise deliverthe prescription drug prior to dispensing, during dispensing, and/orafter dispensing, and/or (iv) the fulfillment process within thepharmacy. Other types of verification information such as barcode dataread from pallets, bins, trays, or carts used to transport prescriptionswithin the pharmacy may also be stored as order data 118.

The member data 120 includes information regarding the membersassociated with the PBM. The information stored as member data 120 mayinclude personal information, personal health information, protectedhealth information, etc. Examples of the member data 120 include name,address, telephone number, e-mail address, prescription drug history,etc. The member data 120 may include a plan sponsor identifier thatidentifies the plan sponsor associated with the member and/or a memberidentifier that identifies the member to the plan sponsor. The memberdata 120 may include a member identifier that identifies the plansponsor associated with the user and/or a user identifier thatidentifies the user to the plan sponsor. The member data 120 may alsoinclude dispensation preferences such as type of label, type of cap,message preferences, language preferences, etc.

The member data 120 may be accessed by various devices in the pharmacy(for example, the high-volume fulfillment center, etc.) to obtaininformation used for fulfillment and shipping of prescription orders. Insome implementations, an external order processing device operated by oron behalf of a member may have access to at least a portion of themember data 120 for review, verification, or other purposes.

In some implementations, the member data 120 may include information forpersons who are users of the pharmacy but are not members in thepharmacy benefit plan being provided by the PBM. For example, theseusers may obtain drugs directly from the pharmacy, through a privatelabel service offered by the pharmacy, the high-volume fulfillmentcenter, or otherwise. In general, the use of the terms “member” and“user” may be used interchangeably.

The claims data 122 includes information regarding pharmacy claimsadjudicated by the PBM under a drug benefit program provided by the PBMfor one or more plan sponsors. In general, the claims data 122 includesan identification of the client that sponsors the drug benefit programunder which the claim is made, and/or the member that purchased theprescription drug giving rise to the claim, the prescription drug thatwas filled by the pharmacy (e.g., the national drug code number, etc.),the dispensing date, generic indicator, generic product identifier (GPI)number, medication class, the cost of the prescription drug providedunder the drug benefit program, the copayment/coinsurance amount, rebateinformation, and/or member eligibility, etc. Additional information maybe included.

In some implementations, other types of claims beyond prescription drugclaims may be stored in the claims data 122. For example, medicalclaims, dental claims, wellness claims, or other types ofhealth-care-related claims for members may be stored as a portion of theclaims data 122.

In some implementations, the claims data 122 includes claims thatidentify the members with whom the claims are associated. Additionallyor alternatively, the claims data 122 may include claims that have beende-identified (that is, associated with a unique identifier but not witha particular, identifiable member).

The drug data 124 may include drug name (e.g., technical name and/orcommon name), other names by which the drug is known, activeingredients, an image of the drug (such as in pill form), etc. The drugdata 124 may include information associated with a single medication ormultiple medications.

The prescription data 126 may include information regardingprescriptions that may be issued by prescribers on behalf of users, whomay be members of the pharmacy benefit plan—for example, to be filled bya pharmacy. Examples of the prescription data 126 include user names,medication or treatment (such as lab tests), dosing information, etc.The prescriptions may include electronic prescriptions or paperprescriptions that have been scanned. In some implementations, thedosing information reflects a frequency of use (e.g., once a day, twicea day, before each meal, etc.) and a duration of use (e.g., a few days,a week, a few weeks, a month, etc.).

In some implementations, the order data 118 may be linked to associatedmember data 120, claims data 122, drug data 124, and/or prescriptiondata 126. The plan sponsor data 128 includes information regarding theplan sponsors of the PBM. Examples of the plan sponsor data 128 includecompany name, company address, contact name, contact telephone number,contact e-mail address, etc.

UI Adaptation

In various implementations, the member data 120 may also include phonecall or internet chat logs for each user and user population. Each logmay include a duration as well as a corresponding claim that the log isreferencing. The member data 120 may also include, for each user, theduration of their membership and the duration of their presence in thepopulation. The duration of membership may include a duration ofpharmacy membership, a duration of inclusion in the user population, andduration of pharmacy membership of each user population.

In various implementations, the claims data 122 may also include costdata for each member or user indicating an amount of cost the pharmacyhas incurred for each user based on customer support, shipping costs,etc. The claims data 122 may also include margin data indicating anintake of the pharmacy for each claim of each user. The intake indicatesan amount the pharmacy has benefited financially for each user. Theoutput and intake may be determined over a period of time.

In various implementations, a web portal 130 includes a personadetermination module 130-1 and a user interface adaptation module 130-2.The web portal 130 is in communication with the user device 108 and aper-user metric determination device 132 via the network 104. A user cancreate an account to manage the user prescriptions via the web portal130. In response to the user logging on to their user account, thepersona determination module 130-1 determines which persona correspondsto the user. This may prompt a per-user metric calculation (or lookup)for the user. The user interface adaptation module 130-2 receives thedetermined persona from the persona determination module 130-1 andtransforms the user interface presented to the user—for example, on theuser device 108—according to the determined persona. In this way, theuser interface presented to the user is personalized.

In various implementations, the persona determination module 130-1determines the persona that corresponds to the user based on theper-user metric of the user. The persona determination module 130-1receives the per-user metric of the user from the per-user metricdetermination device 132. The per-user metric determination device 132receives relevant information of the user from the storage device 110via the network. Based on the received relevant information, theper-user metric determination device 132 determines a retention metricof the user and a benefit metric of the user in order to determine theper-user metric. The persona determination module 130-1 then determinesthe persona that corresponds to the per-user metric.

In various implementations, the storage device 110 is a data store, adata warehouse, a relational database, a NoSQL database, or a datarepository. The storage device 110 is configured to index event data.The event data includes member data 120 and claims data 122. The datastore indexes a plurality of events, where each event corresponds to aphysical object, for example, a prescription drug, being supplied to auser or patient. For example, the prescription drug can be mailed to theuser, the user can obtain the prescription drug from a retail location,such as a pharmacy, etc. In the data store, and in the system, each useror patient can be identified using an identifier unique to theuser/patient.

The data store can further store descriptive data corresponding to eachidentifier to indicate the corresponding user. For example, thisdescriptive data can include demographic data such as age and sex,social information such as histories of smoking and drinking, and healthdata such as present and past medical conditions.

The system 100 includes an analyst device 134 including a personadetermination module 134-1 and a user interface adaptation module 134-2.The system 100 includes a support device 136 including a personadetermination module 136-1 and a user interface adaptation module 136-2.Data analysts of the pharmacy may use the analyst device 134 to runanalytics of specific users or specific user populations based onsubmitted parameters or selected relevant information. In variousimplementations, the pharmacy may analyze potential clients based onsimilar clients.

The analyst device 134 may include a homepage for an analyst to input afuture number of years, a target user or client, and additionalinformation related to the analysis. Based on the determined per-usermetric, the personas may vary according to the analyzed data. Forexample, if the per-user metric is low, a corresponding persona mayadapt the user interface to highlight potential areas of improvement,with respect to the per-user metric, for the user or client.

The support device 136 displays a user interface to a supportrepresentative according to a user or user population that the supportrepresentative is assisting. For example, the support representative maybe at a call center and receive a call from a particular user. Thesupport representative may identify the caller to the support device 136if the phone system had not yet identified the caller before connectingthe caller to the support representative. The persona determinationmodule 136-1 determines a persona of the user based on the submittedparameters and displays a user interface according to the persona. Theuser interface adaptation module 136-2 transforms the user interface. Inthis way, the support representative is displayed a customized userinterface according to the user or the user population of the usercalling for assistance, such as the ability to expedite shipping forfree or at a reduced rate.

The analyst device 134 may also be referred to as a data analyst device,where an analyst can query metrics for one or more users. The metric(s)may be displayed to the analyst, either individually or in theaggregate. In some implementations, the metric(s) may cause the userinterface of the data analyst device to be updated, such as by removinga user interface element or modifying a user interface element (such asby modifying a displayed number or changing a font characteristic).

The web portal 130 may be considered a special case of the data analystdevice, where a first user accessing the data analyst device isrestricted to only information about the first user. Further, in thecase of the web portal 130, the data analyst device may prevent displayof the metric to the first user and instead only modify the userinterface in response to the metric.

The support device 136 may also be considered a special case of the dataanalyst device, where the support representative using the data analystdevice chooses one user at a time (generally, the person whom thesupport representative is interacting with via phone, chat, etc.). Themetric for the chosen user may be displayed to the supportrepresentative. In other implementations, the metric may be used toadapt user interface elements, such as revealing or hiding buttonsrelated to expedited shipping, or modifying text describing the cost orspeed of expedited shipping. In such implementations, the metric may ormay not be shown.

Fulfillment Device

FIG. 2 illustrates the pharmacy fulfillment device 112 according to anexample implementation. The pharmacy fulfillment device 112 may be usedto process and fulfill prescriptions and prescription orders. Afterfulfillment, the fulfilled prescriptions are packed for shipping.

The pharmacy fulfillment device 112 may include devices in communicationwith the benefit manager device 102, the order processing device 114,and/or the storage device 110, directly or over the network 104.Specifically, the pharmacy fulfillment device 112 may include palletsizing and pucking device(s) 206, loading device(s) 208, inspectdevice(s) 210, unit of use device(s) 212, automated dispensing device(s)214, manual fulfillment device(s) 216, review devices 218, imagingdevice(s) 220, cap device(s) 222, accumulation devices 224, packingdevice(s) 226, literature device(s) 228, unit of use packing device(s)230, and mail manifest device(s) 232. Further, the pharmacy fulfillmentdevice 112 may include additional devices, which may communicate witheach other directly or over the network 104.

In some implementations, operations performed by one of these devices206-232 may be performed sequentially, or in parallel with theoperations of another device as may be coordinated by the orderprocessing device 114. In some implementations, the order processingdevice 114 tracks a prescription with the pharmacy based on operationsperformed by one or more of the devices 206-232.

In some implementations, the pharmacy fulfillment device 112 maytransport prescription drug containers, for example, among the devices206-232 in the high-volume fulfillment center, by use of pallets. Thepallet sizing and pucking device 206 may configure pucks in a pallet. Apallet may be a transport structure for a number of prescriptioncontainers, and may include a number of cavities. A puck may be placedin one or more than one of the cavities in a pallet by the pallet sizingand pucking device 206. The puck may include a receptacle sized andshaped to receive a prescription container. Such containers may besupported by the pucks during carriage in the pallet. Different pucksmay have differently sized and shaped receptacles to accommodatecontainers of differing sizes, as may be appropriate for differentprescriptions.

The arrangement of pucks in a pallet may be determined by the orderprocessing device 114 based on prescriptions that the order processingdevice 114 decides to launch. The arrangement logic may be implementeddirectly in the pallet sizing and pucking device 206. Once aprescription is set to be launched, a puck suitable for the appropriatesize of container for that prescription may be positioned in a pallet bya robotic arm or pickers. The pallet sizing and pucking device 206 maylaunch a pallet once pucks have been configured in the pallet.

The loading device 208 may load prescription containers into the puckson a pallet by a robotic arm, a pick and place mechanism (also referredto as pickers), etc. In various implementations, the loading device 208has robotic arms or pickers to grasp a prescription container and moveit to and from a pallet or a puck. The loading device 208 may also printa label that is appropriate for a container that is to be loaded ontothe pallet, and apply the label to the container. The pallet may belocated on a conveyor assembly during these operations (e.g., at thehigh-volume fulfillment center, etc.).

The inspect device 210 may verify that containers in a pallet arecorrectly labeled and in the correct spot on the pallet. The inspectdevice 210 may scan the label on one or more containers on the pallet.Labels of containers may be scanned or imaged in full or in part by theinspect device 210. Such imaging may occur after the container has beenlifted out of its puck by a robotic arm, picker, etc., or may beotherwise scanned or imaged while retained in the puck. In someimplementations, images and/or video captured by the inspect device 210may be stored in the storage device 110 as order data 118.

The unit of use device 212 may temporarily store, monitor, label, and/ordispense unit of use products. In general, unit of use products areprescription drug products that may be delivered to a user or memberwithout being repackaged at the pharmacy. These products may includepills in a container, pills in a blister pack, inhalers, etc.Prescription drug products dispensed by the unit of use device 212 maybe packaged individually or collectively for shipping, or may be shippedin combination with other prescription drugs dispensed by other devicesin the high-volume fulfillment center.

At least some of the operations of the devices 206-232 may be directedby the order processing device 114. For example, the manual fulfillmentdevice 216, the review device 218, the automated dispensing device 214,and/or the packing device 226, etc. may receive instructions provided bythe order processing device 114.

The automated dispensing device 214 may include one or more devices thatdispense prescription drugs or pharmaceuticals into prescriptioncontainers in accordance with one or multiple prescription orders. Ingeneral, the automated dispensing device 214 may include mechanical andelectronic components with, in some implementations, software and/orlogic to facilitate pharmaceutical dispensing that would otherwise beperformed in a manual fashion by a pharmacist and/or pharmacisttechnician. For example, the automated dispensing device 214 may includehigh-volume fillers that fill a number of prescription drug types at arapid rate and blister pack machines that dispense and pack drugs into ablister pack. Prescription drugs dispensed by the automated dispensingdevices 214 may be packaged individually or collectively for shipping,or may be shipped in combination with other prescription drugs dispensedby other devices in the high-volume fulfillment center.

The manual fulfillment device 216 controls how prescriptions aremanually fulfilled. For example, the manual fulfillment device 216 mayreceive or obtain a container and enable fulfillment of the container bya pharmacist or pharmacy technician. In some implementations, the manualfulfillment device 216 provides the filled container to another devicein the pharmacy fulfillment devices 112 to be joined with othercontainers in a prescription order for a user or member.

In general, manual fulfillment may include operations at least partiallyperformed by a pharmacist or a pharmacy technician. For example, aperson may retrieve a supply of the prescribed drug, may make anobservation, may count out a prescribed quantity of drugs and place theminto a prescription container, etc. Some portions of the manualfulfillment process may be automated by use of a machine. For example,counting of capsules, tablets, or pills may be at least partiallyautomated (such as through use of a pill counter). Prescription drugsdispensed by the manual fulfillment device 216 may be packagedindividually or collectively for shipping, or may be shipped incombination with other prescription drugs dispensed by other devices inthe high-volume fulfillment center.

The review device 218 may process prescription containers to be reviewedby a pharmacist for proper pill count, exception handling, prescriptionverification, etc. Fulfilled prescriptions may be manually reviewedand/or verified by a pharmacist, as may be required by state or locallaw. A pharmacist or other licensed pharmacy person who may dispensecertain drugs in compliance with local and/or other laws may operate thereview device 218 and visually inspect a prescription container that hasbeen filled with a prescription drug. The pharmacist may review, verify,and/or evaluate drug quantity, drug strength, and/or drug interactionconcerns, or otherwise perform pharmacist services. The pharmacist mayalso handle containers which have been flagged as an exception, such ascontainers with unreadable labels, containers for which the associatedprescription order has been canceled, containers with defects, etc. Inan example, the manual review can be performed at a manual reviewstation.

The imaging device 220 may image containers once they have been filledwith pharmaceuticals. The imaging device 220 may measure a fill heightof the pharmaceuticals in the container based on the obtained image todetermine if the container is filled to the correct height given thetype of pharmaceutical and the number of pills in the prescription.Images of the pills in the container may also be obtained to detect thesize of the pills themselves and markings thereon. The images may betransmitted to the order processing device 114 and/or stored in thestorage device 110 as part of the order data 118.

The cap device 222 may be used to cap or otherwise seal a prescriptioncontainer. In some implementations, the cap device 222 may secure aprescription container with a type of cap in accordance with a userpreference (e.g., a preference regarding child resistance, etc.), a plansponsor preference, a prescriber preference, etc. The cap device 222 mayalso etch a message into the cap, although this process may be performedby a subsequent device in the high-volume fulfillment center.

The accumulation device 224 accumulates various containers ofprescription drugs in a prescription order. The accumulation device 224may accumulate prescription containers from various devices or areas ofthe pharmacy. For example, the accumulation device 224 may accumulateprescription containers from the unit of use device 212, the automateddispensing device 214, the manual fulfillment device 216, and the reviewdevice 218. The accumulation device 224 may be used to group theprescription containers prior to shipment to the member.

The literature device 228 prints, or otherwise generates, literature toinclude with each prescription drug order. The literature may be printedon multiple sheets of substrates, such as paper, coated paper, printablepolymers, or combinations of the above substrates. The literatureprinted by the literature device 228 may include information required toaccompany the prescription drugs included in a prescription order, otherinformation related to prescription drugs in the order, financialinformation associated with the order (for example, an invoice or anaccount statement), etc.

In some implementations, the literature device 228 folds or otherwiseprepares the literature for inclusion with a prescription drug order(e.g., in a shipping container). In other implementations, theliterature device 228 prints the literature and is separate from anotherdevice that prepares the printed literature for inclusion with aprescription order.

The packing device 226 packages the prescription order in preparationfor shipping the order. The packing device 226 may box, bag, orotherwise package the fulfilled prescription order for delivery. Thepacking device 226 may further place inserts (e.g., literature or otherpapers, etc.) into the packaging received from the literature device228. For example, bulk prescription orders may be shipped in a box,while other prescription orders may be shipped in a bag, which may be awrap seal bag.

The packing device 226 may label the box or bag with an address and arecipient's name. The label may be printed and affixed to the bag orbox, be printed directly onto the bag or box, or otherwise associatedwith the bag or box. The packing device 226 may sort the box or bag formailing in an efficient manner (e.g., sort by delivery address, etc.).The packing device 226 may include ice or temperature sensitive elementsfor prescriptions that are to be kept within a temperature range duringshipping (for example, this may be necessary in order to retainefficacy). The ultimate package may then be shipped through postal mail,through a mail order delivery service that ships via ground and/or air(e.g., UPS, FEDEX, or DHL, etc.), through a delivery service, through alocker box at a shipping site (e.g., AMAZON locker or a PO Box, etc.),or otherwise.

The unit of use packing device 230 packages a unit of use prescriptionorder in preparation for shipping the order. The unit of use packingdevice 230 may include manual scanning of containers to be bagged forshipping to verify each container in the order. In an exampleimplementation, the manual scanning may be performed at a manualscanning station. The pharmacy fulfillment device 112 may also include amail manifest device 232 to print mailing labels used by the packingdevice 226 and may print shipping manifests and packing lists.

While the pharmacy fulfillment device 112 in FIG. 2 is shown to includesingle devices 206-232, multiple devices may be used. When multipledevices are present, the multiple devices may be of the same device typeor models, or may be a different device type or model. The types ofdevices 206-232 shown in FIG. 2 are example devices. In otherconfigurations of the system 100, lesser, additional, or different typesof devices may be included.

Moreover, multiple devices may share processing and/or memory resources.The devices 206-232 may be located in the same area or in differentlocations. For example, the devices 206-232 may be located in a buildingor set of adjoining buildings. The devices 206-232 may be interconnected(such as by conveyors), networked, and/or otherwise in contact with oneanother or integrated with one another (e.g., at the high-volumefulfillment center, etc.). In addition, the functionality of a devicemay be split among a number of discrete devices and/or combined withother devices.

FIG. 3 illustrates the order processing device 114 according to anexample implementation. The order processing device 114 may be used byone or more operators to generate prescription orders, make routingdecisions, make prescription order consolidation decisions, trackliterature with the system 100, and/or view order status and other orderrelated information. For example, the prescription order may becomprised of order components.

The order processing device 114 may receive instructions to fulfill anorder without operator intervention. An order component may include aprescription drug fulfilled by use of a container through the system100. The order processing device 114 may include an order verificationsubsystem 302, an order control subsystem 304, and/or an order trackingsubsystem 306. Other subsystems may also be included in the orderprocessing device 114.

The order verification subsystem 302 may communicate with the benefitmanager device 102 to verify the eligibility of the member and reviewthe formulary to determine appropriate copayment, coinsurance, anddeductible for the prescription drug and/or perform a DUR (drugutilization review). Other communications between the order verificationsubsystem 302 and the benefit manager device 102 may be performed for avariety of purposes.

The order control subsystem 304 controls various movements of thecontainers and/or pallets along with various filling functions duringtheir progression through the system 100. In some implementations, theorder control subsystem 304 may identify the prescribed drug in one ormore than one prescription orders as capable of being fulfilled by theautomated dispensing device 214. The order control subsystem 304 maydetermine which prescriptions are to be launched and may determine thata pallet of automated-fill containers is to be launched.

The order control subsystem 304 may determine that an automated-fillprescription of a specific pharmaceutical is to be launched and mayexamine a queue of orders awaiting fulfillment for other prescriptionorders, which will be filled with the same pharmaceutical. The ordercontrol subsystem 304 may then launch orders with similar automated-fillpharmaceutical needs together in a pallet to the automated dispensingdevice 214. As the devices 206-232 may be interconnected by a system ofconveyors or other container movement systems, the order controlsubsystem 304 may control various conveyors: for example, to deliver thepallet from the loading device 208 to the manual fulfillment device 216from the literature device 228, paperwork as needed to fill theprescription.

The order tracking subsystem 306 may track a prescription order duringits progress toward fulfillment. The order tracking subsystem 306 maytrack, record, and/or update order history, order status, etc. The ordertracking subsystem 306 may store data locally (for example, in a memory)or as a portion of the order data 118 stored in the storage device 110.

Per-User Determination Controller

FIG. 4 is a functional block diagram of an example per-user metricdetermination device 132. The per-user metric determination device 132receives input parameters and accesses member data 120 and claims data122 from the storage device 110 to calculate a per-user metric of auser, depending on the input parameters. While the present applicationrefers to the per-user metric of the user, a population-wide metric ofthe user population may also be determined using the per-user metricdetermination device 132.

In various implementations, for each per-user metric calculation, theper-user metric determination device 132 may identify multipleprocessing threads on each of multiple processor cores and assigncalculations of the per-user metric calculation to respective threads.To calculate the per-user metric, a retention module 400 receives inputparameters. For example, when a selected user logs into their account,the retention module 400 may receive input parameters from an account ofthe selected user. In such an implementation, the user identity may bereceived in response to the selected user logging into their account. Invarious implementations, the input parameters include the user identityand the current user population. While using an analyst device, ananalyst may input the user identity along with the user population). Theuser population may indicate demographic data or employment informationof the selected user. For example, the employees of the selected user'semployer may form the user population.

The retention module 400 calculates the amount of time the user and theuser population was retained as a patient of the organization during atime period—for example, the prior year—based on the user accountinformation and user duration data maintained in the member data 120. Invarious implementations, the retention module 400 receives member data120 from the storage device 110 to obtain duration data for each memberof the user population.

The per-user metric determination device 132 also includes an expectedretention module 404. The expected retention module 404 receives theinput parameters, the member data 120, and the actual user/populationretention calculated by the retention module 400. The expected retentionmodule 404 identifies relevant demographic data in the member data 120.The relevant demographic data may include the age of the user andemployment information of the user. The expected retention module 404can compare the user's age, employment information, and actualhistorical retention to historical retention rates of users in similarcircumstances.

In various implementations, similar circumstances may be determined fromdemographic data and may include an age range, employer, etc. Thehistorical retention rates of users in similar circumstances may bedetermined from data included in the member data 120. The expectedretention module 404 calculates the expected user retention and theexpected population retention that includes the selected user based onhistorical retention rates of the selected user, the population of theselected user, and retention of users in similar circumstances. Forexample, if users in an age range of 20-25 years have an actualhistorical retention rate of 80%, the expected retention module 404 maydetermine the same expected retention rate of 80% for a 23-year-old userwhen comparing the 23-year-old user to users in the respective agerange.

An expected output module 408 receives member data 120 and claims data122 as well as the input parameters. In various implementations, theexpected output module 408 also receives historical claims data of aselected training group of approved members in the claims data 122.Members are identified as approved members if the member data (as wellas other data corresponding to the member) is allowed to be included indata processing and modeling. For example, certain members may beexcluded from data processing and modeling based on restrictions on theuse of their healthcare information.

The selected training group, for example, may include historical claimsof 500,000 approved users for a previous time period, such as the prioryear. The expected output module 408 obtains behavioral data for eachuser in the training group from the member data 120. For example, theexpected output module 408 identifies a number of times each user in thetraining group has called a support center of the pharmacy per claimused any other resource of the pharmacy (such as requesting shipment ofdrugs) over the previous period of time. The expected output module 408quantifies each use to determine a previous output of each user in thetraining group. The previous output of each user in the training groupindicates an output or cost required to serve the respective user in thetraining group. The output may include all costs incurred to serve theuser.

Similarly, the expected output module 408 determines previous outputs ofeach user of the training group during the previous period of time. Invarious implementations, an output model of the training group isincluded in the expected output module 408. The output model is atrained machine learning model that predicts future output: for example,for the next year. The training group is used to train the output modeland a test group—for example, of the same size or smaller than thetraining group—is used to verify the accuracy of the output model. Theexpected output module 408 determines an expected output of the userbased on the output model and previous user output over a future timeperiod, where the future time period is an input parameter. For example,the future time period for the web portal may be a predetermined value,such as one year. Meanwhile, an analyst may choose the future timeperiod according to their analytical task.

The per-user metric determination device 132 also includes an expectedintake module 412. The expected intake module 412 indicates an amountreceived by the pharmacy based on the claims the user has made over theprevious time period. The expected intake module 412 receives memberdata 120 and claims data 122 as well as the input parameters. In variousimplementations, the expected output module 408 and the expected intakemodule 412 are included in one module. Similar to the expected outputmodule 408, the expected intake module 412 includes an intake modelcreated according to a machine learning method. The intake model is atrained model that predicts future intake: for example, for the nextyear. In various implementations, the intake represents margin orresources received from a user for an event: for example, an amount paidby the user and/or another payer (such as a health plan) for receiving adrug. In various implementations, the intake may represent the amountthe user paid for the drug less the amount the pharmacy expended toprocure, handle, store, and ship the drug.

The output model and the intake model are trained on a regular basisinstead of being trained each time a metric is calculated. The intakemodel is created from historical claims data and extracts claims toanalyze the intake from each claim for each user over the previous timeperiod. Based on the input parameters, the expected intake module 412determines an expected intake of the user based on the intake model andprevious user intake over a future time period, where the future timeperiod is an input parameter.

The expected retention module 404, the expected output module 408, andthe expected intake module 412 may all include machine learning modelsgenerated by a data processing circuit. For example, the data processingcircuit may be included in the per-user metric determination device 132or within each of the previously listed modules. To generate therespective models, a set of users is identified. A machine learningmodel is trained based on event data of the set of identified users overa predetermined previous period or epoch—for example, the previous year.The machine learning model for the respective modules can identifyrelevant information included in the event data. For example, a modelgenerated for or by the expected output module 408 may identifyinformation regarding an output of each event to train the model basedon previous output received for the set of identified users over thepredetermined previous period.

As previously mentioned, in various implementations, the machinelearning model may be trained using parallel processing for recordsobtained from the storage device 110. The parallel processing includesassigning the analysis of the stored event data for each set ofidentified users to respective processor threads for parallel executionon processing hardware. Using parallel processing to train the modelsdescribed above is a new and efficient method to train models faster.

A per-user metric calculation module 416 receives the input parameters,the expected user retention and the expected population retention fromthe expected retention module 404, the expected output of the user fromthe expected output module 408, and the expected intake of the user fromthe expected intake module 412. The per-user metric calculation module416 calculates a per-user metric of the user based on the receivedinformation. In various implementations, the per-user metric calculationmodule 416 calculates the per-user metric according to the followingequation:

${PUM}_{k} = {A_{k}{\sum\limits_{i = 1}^{n}\frac{{G_{i}\left( {M_{k} - C_{k}} \right)}r^{i}}{\left( {1 + d} \right)^{i}}}}$

where PUM_(k) is the per-user metric of the user identified by aninteger identifier k, A_(k) is the starting retention age of the user, nis the number of future years to consider, G_(i) is the drop in userretention at the future year i based on the expected user retention,M_(k) is the expected intake of the user, C_(k) is the expected outputof the user, r is the expected population retention rate of the userpopulation, and d is a discount rate that a user or user population mayreceive. The per-user metric is output to the requesting device, such asthe web portal.

The per-user metric determination device 132 also includes a per-usermetric storage 420. The per-user metric storage 420 can be accessed viathe per-user metric calculation module 416. If the per-user metric ofthe user has already been calculated, the per-user metric calculationmodule 416 may retrieve the per-user metric of the user from theper-user metric storage 420. Each time a per-user metric is calculated,the per-user metric may be stored in the per-user metric storage 420.

In various implementations, the per-user metric determination device 132can determine the per-user metric of the user during a previous time.That is, the input parameters may specify a retrospective time period.The per-user metric determination device 132 will access historical dataincluded in the storage device 110 of the corresponding user. In thisway, a comparison may be made between the per-user metric of the userduring the previous time and the current per-user metric of the user.

In various implementations, the per-user metric determination device 132may be an interface circuit capable of performing all the functions ofthe per-user metric determination device 132 described above. Similarly,the per-user metric may be described as an interface metric.

Flowcharts

FIG. 5 is a flowchart of example determination of a per-user metricrequest. Control begins at in response to a per-user metric request. Forexample, control may begin after receiving a per-metric request for aspecified user. Once the request is received, control continues to 504where control determines if the per-user metric is available for thespecified user. For example, if the per-user metric was previouslycalculated and stored in an accessible storage, then the per-user metricis available. If the per-user metric is available, control continues to508; otherwise, control continues to 512. At 508, control determineswhether the per-user metric is out of date. If so, control transfers to514; otherwise, control transfers to 512.

At 512, control obtains input parameters. For example, input parametersmay include a user identity, the requesting device (the web portal, thesupport device, the analyst device, etc.), a discount rate of the user,etc. Control then continues to 516 where the relevant population of theuser is identified from a time period, for example, a prior year.

Once the population relevant to the user is identified, controlcontinues to 520, where the initial user is selected from the identifiedpopulation. Control proceeds to 524, where a duration of presence of theselected user in the identified population is determined. Control thencontinues to 528 to compute a percentage of the time period that theselected user was retained. That is, control calculates the amount oftime the selected user was included in the identified population duringthe time period. In this way, the amount of time the user was retainedin the identified population during the time period is calculated.

Control continues to 532 to store the completed retention metric of theselected user. Control proceeds to 536 to determine if another user isincluded in the identified population. If so, control proceeds to 540where the next user of the identified population is selected. Controlthen returns to 524 to determine the duration of presence of theselected user in the identified population. If, at 536, there are noadditional users to evaluate in the identified population, controlcontinues to 544, where control calculates the population retentionmetric based on the stored retention metrics of users in the identifiedpopulation. In this way, individual retention rates are calculated alongwith the retention rate of the entire population.

Control proceeds to 548 where control calculates the per-user metric forthe specified user. FIG. 6 describes an example of per-user metriccalculation. Once calculated, the per-user metric for the specified useris stored at 552. Control proceeds to 514 where the per-user metric ofthe specified user is graphically displayed and control ends. In variousimplementations, the analyst device described in FIG. 1 calculates theper-user metric and displays the per-user metric along with atransformed user interface for the persona corresponding to the per-usermetric.

FIG. 6 is a flowchart of an example per-user metric calculation. Controlbegins at 604, where the stored claims data of the specified user isobtained. Control continues to 608 where relevant claims for thespecified user are selected from the claims data. At 612, controlcalculates the expected intake of the specified user based on theselected claims. Then at 616, control obtains stored member data of thespecified user, such as from a storage device. Control proceeds to 620,where relevant member data of the specified user is selected. At 624,control calculates an expected output of the specified user based on theselected member data.

Then, at 628, control selects relevant demographic data of the specifieduser from the member data. Control continues to 632, where expected userretention metric is calculated based on the selected demographic dataand actual user retention metric. For example, the actual user retentionmay be calculated as shown in FIG. 5. At 636, control calculates theper-user metric of the specified user based on the expected intake, theexpected output, the expected population retention metric, and theexpected user retention metric of the specified user.

FIG. 7 is a flowchart of an example persona selection for a web portal.Control begins in response to a user logging on to the web portal. Oncea user has logged on to the web portal, control proceeds to 704 andobtains the per-user metric of the logged-on user, as described in FIG.6. Control then continues to 708 to determine if the per-user metric isgreater than a metric threshold. If not, control proceeds to 712 todetermine if a user retention is greater than a retention threshold. Invarious implementations, the user retention may be a measure of actualuser retention over the prior year. As another example, the userretention may be an expected user retention for the upcoming year. Ifthe user retention is not greater than the retention threshold, controlcontinues to 716 and selects persona 1. Persona 1 represents a user witha low per-user metric as well as a low retention. When the usercorresponds to persona 1, the user interface will be modified toencourage the user to achieve a higher per-user metric and/or higheruser retention. The user interface may also be modified to removehigher-overhead items, such as free expedited shipping, for the userwhose retention is unlikely to be affected by such perks. Once persona 1is selected at 716, control continues to 720 to transform the userinterface on the web portal to correspond to the identified persona, andcontrol ends.

Returning to 712, if the user retention is greater than the retentionthreshold, control continues to 724 and selects persona 2. Persona 2represents a user with a low per-user metric and a high user retention.When the user corresponds to persona 2, the user interface will bemodified to encourage the user to achieve a higher per-user metric andmay offer benefits based on the high user retention. Once persona 2 isselected, control continues to 720.

Returning to 708, if the per-user metric is greater than the metricthreshold, control continues to 728. At 728, control determines if theuser retention is greater than a retention threshold. If not, controlcontinues to 732 and selects persona 3. Persona 3 represents a user witha high per-user metric but a low user retention. Similar to persona 2,the user interface will be modified to encourage the user to achievehigher user retention but may provide or suggest certain benefits basedon the high per-user metric. Once persona 3 is selected, controlcontinues to 724.

Returning to 728, if the user retention is greater than the retentionthreshold, control continues to 736 and selects persona 4. Persona 4represents a user with a high per-user metric as well as a high userretention. Therefore, the user interface may offer certain benefits tothe user based on the high per-user metric and high user retention. Oncepersona 4 is selected, control continues to 724.

In various implementations, the user interface options for each personainclude two variations of the user interface that are displayed tousers. The users for each persona will be categorized into two groups. Afirst variation of a corresponding persona will be displayed to a firstgroup and a second variation of the corresponding persona will bedisplayed to a second group. In various implementations, users areassigned to each group randomly. The per-user metric of each user ofeach group will be monitored over a testing period—for example, onemonth. At the end of the testing period, the per-user metric of eachuser at the end of the testing period is compared to the per-user metricof each user at the beginning of the testing period. Then, the averageper-user metric of the first group is compared to the average per-usermetric of the second group at the beginning and end of the testingperiod. The variation that corresponds to the group that has the mostimproved averaged per-user metric by the end of the testing period isselected and used for all users of the corresponding persona.

FIG. 8 is a flowchart of an example persona selection for a supportdevice. Control begins in response to a support representative loggingonto the support device. As mentioned previously, the present disclosureapplies to calculating a per-user metric as well as a population metric.For purposes of FIG. 8, the flowchart will describe calculating anaverage metric of the population. However, on average FIG. 8 may insteadbe implemented with an individual per-user metric and a populationretention value.

Once the support representative has logged on to the support device,control continues to 804 and identifies a population. For example, thesupport representative may have received a phone call and whileconducting the phone call the support representative may input theidentity of the user that is calling. The population is then identifiedbased on the user identity, such as the prescription plan to which theuser belongs. Control continues to 808 to obtain a per-user metric ofeach user in the identified population. For example, FIG. 5 describescalculating population retention, and FIG. 6 describes how to calculatea per-user metric of a particular user.

Control then continues to 812 to calculate the average per-user metricof users in the identified population and the population retention. Oncethe average is calculated, control continues to 816 to determine if theaverage per-user metric is greater than a metric threshold. If not,control continues to 822 determine if the population retention of theidentified population is greater than a retention threshold. If not,control continues to 824 and selects persona 1.

Control continues to 828, where the user interface on the support deviceis transformed according to the identified persona, and control ends.When a persona is being determined for the support device, the personasmay be different from the personas selected for the web portal. Forexample, when the identified population has a low average per-usermetric and a low retention—that is, when control selects persona one—theuser interface of the support device may display fewer benefit optionsfor the support representative to offer the identified population.Further, for every persona, the support device may display the averageper-user metric of the identified population as well as the populationretention to inform the support representative of these metrics.Additionally, when the persona indicates a higher average per-usermetric or a higher retention likelihood (such as persona 4), the userinterface of the support device may offer more benefit options for thesupport representative to offer the user.

Returning to 820, if the population retention is greater than theretention threshold, then control continues to 832 and selects persona2. Control then continues to 828. Returning to 816, if the averageper-user metric is greater than the metric threshold, then controlcontinues to 836. At 836, control determines if the population retentionis greater than the retention threshold. If so, control continues to840, selects persona 4, and continues to 828. Otherwise, controlcontinues to 844, selects persona 3, and continues to 828.

User Interface

FIG. 9A is an example user interface adaptation of a prescriptioninterface, such as a homepage or application screen for a low per-usermetric. In various implementations, a user accesses the prescriptioninterface using a user device 900. The user device 900 may be a desktopcomputer, kiosk, or mobile computing device, such as a phone or tablet.The user device 900 includes a display screen 904, which displays theprescription interface. In various implementations, when a user has alow per-user metric as well as a low user retention (personal), the userinterface of the prescription interface will provide encouragement forthe user to increase their per-user metric as well as their retentionlikelihood.

For example, the prescription interface may include a pendingprescription refills area 908 that includes a list of pending refills.The prescription interface may also include an advertisement for onlinechatting with support representatives 912. For example, as shown theadvertisement may read “Questions? Chat Online Now!” The prescriptioninterface may also include a membership advertisement 916. Themembership advertisement 916 may be a rewards program based on usercommitment, such as automatic refills of prescriptions.

In various implementations, each of the user interface adaptationmodules 130-2, 134-2, and 136-2 of FIG. 1 are configured to perform amodification of a user interface element on the user interface of therespective device based on the per-user metric. For example, as shown inFIG. 9A, the prescription interface may include a pending prescriptionrefills area 908 that includes a list of pending refills. Theprescription interface may also include an advertisement for onlinechatting with support representatives 912. For example, as shown theadvertisement may read “Questions? Chat Online Now!” The prescriptioninterface may also include a membership advertisement 916. Themembership advertisement 916 may be a rewards program based on usercommitment, such as automatic refills of prescriptions. The modificationof the instant user interface may be altering the online chatadvertisement to offer the option of calling a support representative inresponse to the per-user metric of the user. Alternatively, in variousimplementations, the advertisement user interface element may be removedfrom the user interface.

FIG. 9B is an example user interface adaptation of a prescriptioninterface for a user with a high per-user metric. The display screen 904of the user device 900 will display a different user interface for usersthat have a higher per-user metric as well as a higher user retentionlikelihood, as shown in FIG. 9B. For example, a pending prescriptionrefills area 920 may include a list of pending refills. In variousimplementations, for certain upcoming prescription refills, an expediteshipping button 924 may appear if the pending prescription refill dateis quickly approaching. Further, for the user with a higher per-usermetric, an advertisement for the user to call, rather than simply chatwith, a support representative 928 may appear on the prescriptioninterface. For example, the advertisement may read “Questions? CallNow!” Additionally, an advertisement to redeem loyalty rewards 932 mayalso appear based on the user's commitment to the pharmacy.

Per-User Determination Controller

In various implementations, the order data 118, member data 120, and/orclaims data 122 may indicate whether each user is receiving theprescription order through a mail order service. For example, the orderdata 118, member data 120, and/or claims data 122 for the user maycontain a code indicating that the user is participating in the mailorder system. In some examples, the order data 118, member data 120,and/or claims data 122 for the user may contain machine-readableinstructions, such as a code or instructions to the packing device 226to label and/or sort the box or bag containing the fulfilledprescription order for delivery to the recipient through postal mail,through the mail order delivery system that ships via ground and/or air,through the delivery system, or through a locker box at the shippingsite, or likewise.

In various implementations, the order data 118, member data 120, and/orclaims data 122 for the user may contain machine-readable instructions,such as code or instructions to the mail manifest device 232 to printmailing labels for the user. According to example embodiments, for usersnot participating in the mail order service (for example, if the user isparticipating in a program to receive the prescription order in-personat a retail location such as a pharmacy), the member data 120 may notcontain the code indicating that the member is participating in the mailorder system, and/or machine readable instructions to the packing device226 and/or mail manifest device 232.

In various implementations, the order data 118, member data 120, and/orclaims data 122 for the user may contain data indicative of whether theuser's prescription order is currently being filled with a name-brandprescription drug or a generic prescription drug. In variousimplementations, the order data 118, member data 120, and/or claims data122 for the user may contain data indicative of whether at least a90-day supply of the prescription drug is being filled for the user witheach order. In various implementations, the order data 118, member data120, and/or claims data 122 for the user may contain data indicative ofwhether the user is on a step therapy plan. For example, for users on astep therapy plan, less expensive drugs may be first tried before“stepping up” to more expensive drugs.

In various implementations, the expected intake module 412 may include atrained machine learning model that predicts an increase in the numberof claims the pharmacy may be expected to receive from the user for anygiven year in the future. For example, the expected intake module 412may receive at least one of the order data 118, member data 120, claimsdata 122, as well as additional input parameters, and use the trainedmachine learning model to calculate an expected number of additionalclaims the pharmacy may be expected to receive from the user for anygiven year in the future. The expected intake module 412 may include theexpected number of additional claims as an additional input for theintake model to calculate an updated predicted future intake of theuser.

In various implementations, the expected intake module 412 may calculatea modified expected intake of the user based on modified order data 118,member data 120, and/or claims data 122. For example, if the order data118, member data 120, and/or claims data 122 do not contain codeindicating that the member is participating in the mail order system,and/or machine readable instructions to the packing device 226 and/ormail manifest device 232, the expected intake module 412 may modify therelevant member data in order to perform what-if analysis. For example,the expected intake module 412 may modify relevant code or instructionsin the member data to indicate that the user is participating in themail order system, and/or for the packing device 226 to label and/orsort the box or bag for delivery through mail or delivery, and/or forthe mail manifest device 232 to print mailing labels for the user. Theexpected intake module 412 may then calculate a modified expected intakeof the user based on the modified order data 118, member data 120,and/or claims data 122.

In various implementations, if the order data 118, member data 120,and/or claims data 122 do not indicate that the member's prescriptionorder is currently being filled with a name-brand prescription drug, theexpected intake module 412 may modify the relevant member data toindicate that the member's prescription order is being filled with aname-brand prescription drug in order to perform the what-if analysis.The expected intake module 412 may then calculate a modified expectedintake of the user based on the modified order data 118, member data120, and/or claims data 122.

In various implementations, if the order data 118, member data 120,and/or claims data 122 do not indicate that at least a 90-day supply ofthe prescription drug is being filled for the member with eachprescription order, the expected intake module 412 may modify therelevant member data so that each prescription order contains at least a90-day supply of the prescription drug in order to perform the what-ifanalysis. The expected intake module 412 may then calculate a modifiedexpected intake of the user based on the modified order data 118, memberdata 120, and/or claims data 122.

In various implementations, if the order data 118, member data 120,and/or claims data 122 do not indicate that the member is on a steptherapy plan, the expected intake module 412 may modify the relevantmember data to indicate that the member is on a step therapy plan inorder to perform the what-if analysis. The expected intake module 412may then calculate a modified expected intake of the user based on themodified order data 118, member data 120, and/or claims data 122.

Additional Flowcharts

FIG. 10 is a flowchart of example determinations of a per-user metricrequest. In various implementations, control begins at 1004 in responseto receiving a request for a per-user metric for a specified user. At1004, control determines whether the per-user metric is available. Insome examples, if the per-user metric was previously calculated andstored in an accessible storage, then the per-user metric may beavailable. If the per-user metric is available, control continues to1008; otherwise, control continues to 1012. At 1008, control determineswhether the per-user metric is out of date. If control determines thatthe per-user metric is out of date, control transfers to 1012;otherwise, control transfers to 1016.

At 1012, control obtains input parameters. The input parameters mayinclude a user identity, the requesting device (e.g., the web portal,the support device, the analyst device, etc.), a discount rate of theuser, and/or other relevant parameters. Control then proceeds to 1020.

At 1020, control obtains target parameters. In various implementations,target parameters may include whether the user is participating in themail order system. In various implementations, target parameters mayinclude machine-readable instructions, such as a code or instructions tothe packing device 226 to label and/or sort the box or bag containingthe fulfilled prescription order for delivery to the recipient throughpostal mail, a mail order delivery system, through a locker box, orlikewise. In various implementations, the target parameters may includemachine-readable instructions, such as code or instructions to the mailmanifest device 232 to print mailing labels for the user. Control thenproceeds to 1024.

At 1024, control identifies the population relevant to the specifieduser from a time period (for example, a prior year). Once the relevantpopulation is identified, control proceeds to 1028. At 1028, controlselects the first user from the identified population. Control thenproceeds to 1032. At 1032, control calculates a duration of presence ofthe selected user in the identified population. Control then continuesto 1036. At 1036, control computes a percentage of the time period thatthe selected user was retained. In various implementations, control maycalculate the amount of time the selected user was included in theidentified population during the time period. In variousimplementations, the amount of time the user was retained in theidentified population during the time period is calculated. Control thenproceeds to 1040.

At 1040, control stores the completed retention metric of the selecteduser. Control then proceeds to 1044 to determine whether another user isincluded in the identified population. If at 1044 control determinesthat another user is included in the identified population, controlproceeds to 1048 where the next user of the identified population isselected. Control then returns to 1032 to determine the duration ofpresence of the selected user in the identified population. If, at 1044,control determines that there are no additional users to evaluate in theidentified population, control continues to 1052, where controlcalculates the population retention based on the stored retention ofusers in the identified population. For example, individual retentionrates may be calculated along with the retention rate of the entirepopulation. Control then proceeds to 1016.

At 1016, control determines whether the target parameter is true for thespecified user. In various implementations, the target parameter may bea parameter obtained at 1020. If the target parameter is true for thespecified user, then the specified user may be participating in the mailorder system. In some examples, if the target parameter is true for thespecified user, code or instructions may be present for the packingdevice 226 to label and/or sort the box or bag containing the fulfilledprescription order for delivery to the recipient through postal mail, amail order delivery system, through a locker box, or likewise. Invarious implementations, if the target parameter is true for thespecified user, code or instructions may be present for the mailmanifest device 232 to print mailing labels for the specified user. Invarious implementations, if the target parameter is true for thespecified user, data may be present indicating that the specified user'sprescription order is currently being filled with a name-brandprescription drug, that at least a 90-day supply of the prescriptiondrug is being filled for the user with each order, and/or that thespecified user is on a step therapy plan. If, at 1016, controldetermines that the target parameter is true for the specified user,control then proceeds to 1056; otherwise, control proceeds to 1060.

At 1056, control may calculate the per-user metric for the specifieduser according to the example depicted in FIG. 11. Once calculated, theper-user metric for the specified user is stored at 1064. Control thenproceeds to 1068 where the per-user metric of the specified user isgraphically displayed and control ends. In various implementations, theanalyst device 134 calculates the per-user metric and/or display theper-user metric. In some examples, the support device 136 calculates theper-user metric and/or displays the per-user metric.

At 1056, control calculates the per-user metric for the specified useras well as a projected per-user metric for the specified user accordingto the example depicted in FIG. 12. Once calculated, the per-user metricand projected per-user metric for the specified user are stored at 1072.Control then proceeds to 1076, where the per-user metric of thespecified user is graphically displayed. Control continues to 1080,where the projected per-user metric of the specified user is graphicallydisplayed and control ends. In various implementations, the analystdevice described in FIG. 1 calculates the per-user metric and projectedper-user metric, and displays the per-user metric and projected per-usermetric.

FIG. 11 is a flowchart of an example per-user metric calculation.Control begins at 1104, where the stored claims data of the specifieduser is obtained. Control continues to 1108, where relevant claims forthe specified user are selected from the claims data. At 1112, controlcalculates the expected intake of the specified user based on theselected claims. Then at 1116, control calculates the predicted futureintake of the specified user based on the selected claims. At 1120,control obtains stored member data of the specified user, for example,from a storage device. Control then proceeds to 1124, where relevantmember data of the specified user may be selected. At 1128, controlcalculates an expected output of the specified user based on theselected member data. Control then proceeds to 1132.

At 1132, control selects relevant demographic data of the specified userfrom the member data. Control then continues to 1136, where the expecteduser retention metric may be calculated based on the selecteddemographic data and actual user retention metric. In variousimplementations, the actual user retention metric may be calculated asshown in FIG. 5. At 1140, control calculates the per-user metric of thespecified user based on the expected intake, the expected output, thepredicted future intake, the expected population retention metric, andthe expected user retention metric of the specified user.

FIG. 12 is a flowchart of an example per-user metric calculation andexample projected per-user metric calculation. Control begins at 1204,where the stored claims data of the specified user is obtained. Controlthen proceeds to 1208, where relevant claims for the specified user isselected from the claims data. At 1212, control calculates the expectedintake of the specified user based on selected claims. At 1216, controlcalculates the predicted future intake of the specified user based onselected claims. Control then continues to 1220, where control obtainsstored member data of the specified user. At 1224, control then selectsrelevant member data of the specified user. Control then proceeds to1228 and calculates the expected output of the specified user based onthe selected member data. At 1232, control selects relevant demographicdata of the specified user from member data. Control then continues to1236.

At 1236, control calculates the expected user retention metric based onthe selected demographic data and actual user retention metric. In someexamples, control may calculate the actual user retention metricaccording to the method of FIG. 5. At 1240, control calculates theper-user metric based on the expected intake, expected output, predictedfuture intake, expected population retention metric, and expected userretention metric. Control then proceeds to 1244, where control selectsrelevant member data of the specified user and modifies the selectedrelevant member data. For example, control may select and modifyrelevant code or instructions in the member data to indicate that theuser is participating in the mail order system. In variousimplementations, control may select and modify relevant code orinstructions for the packing device 226 to label and/or sort the box orbag for delivery through mail or delivery. In various implementations,control selects and modifies relevant code or instructions for the mailmanifest device 232 to print mailing labels for the user. In variousimplementation, control selects and modifies relevant data to indicatethat the user's order is being filled with a name-brand prescriptiondrug, that each order contains at least a 90-day supply of theprescription drug, and/or that the user is on a step therapy plan.Control then proceeds to 1248.

At 1248, control calculates the modified expected intake of thespecified user based on the modified selected relevant member data. At1252, control calculates the projected per-user metric. In variousimplementations, the projected per-user metric may be calculated basedon the expected intake, modified expected intake, predicted futureintake, expected population retention metric, and expected userretention metric.

FIG. 13 is a flowchart of an example process for displaying an exampleper-user metric and projected per-user metric on a user interface of theanalyst device 134 or support device 136. In various implementations,control begins at 1304 in response to a support representative oranalyst logging on to the analyst device 134 or support device 136. At1304, control obtains the target parameters of the user. At 1308,control determines whether the target parameter is true for the user. Ifso, control proceeds to 1312; otherwise, control proceeds to 1316. At1312, control obtains the per-user metric of the user. For example,control may obtain the per-user metric calculated according to FIG. 11.Control continues to 1320, where control displays the per-user metric ofthe user.

At 1316, control obtains the per-user metric of the user. In variousimplementations, control may obtain the per-user metric calculatedaccording to FIG. 11 or FIG. 12. Control continues to 1324. At step1324, control obtains the projected per-user metric of the user.According to illustrative implementations, control may obtain theprojected per-user metric of the user calculated according to FIG. 12.Control then proceeds to 1328. At 1328, control determines if theprojected per-user metric is greater than the per-user metric. If at1328 control determines that the projected per-user metric is notgreater than the per-user metric, control proceeds to 1332; otherwise,proceed to 1336.

At 1332, control displays the per-user metric of the user. For example,control may display the per-user metric of the user on the userinterface of the analyst device 134. In various implementations, controldisplays the per-user metric of the user on the user interface of thesupport device 136.

At 1336, control calculates the difference between the projectedper-user metric and the per-user metric. At 1340, control displays theper-user metric of the user. At 1344, control displays the projectedper-user metric of the user. At 1348, control displays the calculateddifference between the projected per-user metric and the per-usermetric. In various implementations, the per-user metric, the projectedper-user metric, and/or the calculated difference between the projectedper-user metric and the per-user metric may be displayed on the userinterface of the analyst device 134. In various implementations, theper-user metric, the projected per-user metric, and/or the calculateddifference between the projected per-user metric and the per-user metricmay be displayed on the user interface of the support device 136.

CONCLUSION

The foregoing description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Thebroad teachings of the disclosure can be implemented in a variety offorms. Therefore, while this disclosure includes particular examples,the true scope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure can be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Spatial and functional relationships between elements (for example,between modules) are described using various terms, including“connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitlydescribed as being “direct,” when a relationship between first andsecond elements is described in the above disclosure, that relationshipencompasses a direct relationship where no other intervening elementsare present between the first and second elements, and also an indirectrelationship where one or more intervening elements are present (eitherspatially or functionally) between the first and second elements. Asused herein, the phrase at least one of A, B, and C should be construedto mean a logical (A OR B OR C), using a non-exclusive logical OR, andshould not be construed to mean “at least one of A, at least one of B,and at least one of C.”

In the figures, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. Further, for information sentfrom element A to element B, element B may send requests for, or receiptacknowledgements of, the information to element A. The term subset doesnot necessarily require a proper subset. In other words, a first subsetof a first set may be coextensive with (equal to) the first set.

In this application, including the definitions below, the term “module”or the term “controller” may be replaced with the term “circuit.” Theterm “module” may refer to, be part of, or include processor hardware(shared, dedicated, or group) that executes code and memory hardware(shared, dedicated, or group) that stores code executed by the processorhardware.

The module may include one or more interface circuits. In some examples,the interface circuit(s) may implement wired or wireless interfaces thatconnect to a local area network (LAN) or a wireless personal areanetwork (WPAN). Examples of a LAN are Institute of Electrical andElectronics Engineers (IEEE) Standard 802.11-2016 (also known as theWIFI wireless networking standard) and IEEE Standard 802.3-2015 (alsoknown as the ETHERNET wired networking standard). Examples of a WPAN arethe BLUETOOTH wireless networking standard from the Bluetooth SpecialInterest Group and IEEE Standard 802.15.4.

The module may communicate with other modules using the interfacecircuit(s). Although the module may be depicted in the presentdisclosure as logically communicating directly with other modules, invarious implementations the module may actually communicate via acommunications system. The communications system includes physicaland/or virtual networking equipment such as hubs, switches, routers, andgateways. In some implementations, the communications system connects toor traverses a wide area network (WAN) such as the Internet. Forexample, the communications system may include multiple LANs connectedto each other over the Internet or point-to-point leased lines usingtechnologies including Multiprotocol Label Switching (MPLS) and virtualprivate networks (VPNs).

In various implementations, the functionality of the module may bedistributed among multiple modules that are connected via thecommunications system. For example, multiple modules may implement thesame functionality distributed by a load balancing system. In a furtherexample, the functionality of the module may be split between a server(also known as remote, or cloud) module and a client (or, user) module.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. Shared processor hardware encompasses asingle microprocessor that executes some or all code from multiplemodules. Group processor hardware encompasses a microprocessor that, incombination with additional microprocessors, executes some or all codefrom one or more modules. References to multiple microprocessorsencompass multiple microprocessors on discrete dies, multiplemicroprocessors on a single die, multiple cores of a singlemicroprocessor, multiple threads of a single microprocessor, or acombination of the above.

Shared memory hardware encompasses a single memory device that storessome or all code from multiple modules. Group memory hardwareencompasses a memory device that, in combination with other memorydevices, stores some or all code from one or more modules.

The term memory hardware is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium is therefore considered tangible and non-transitory. Non-limitingexamples of a non-transitory computer-readable medium are nonvolatilememory devices (such as a flash memory device, an erasable programmableread-only memory device, or a mask read-only memory device), volatilememory devices (such as a static random access memory device or adynamic random access memory device), magnetic storage media (such as ananalog or digital magnetic tape or a hard disk drive), and opticalstorage media (such as a CD, a DVD, or a Blu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks andflowchart elements described above serve as software specifications,which can be translated into the computer programs by the routine workof a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory computer-readable medium. Thecomputer programs may also include or rely on stored data. The computerprograms may encompass a basic input/output system (BIOS) that interactswith hardware of the special purpose computer, device drivers thatinteract with particular devices of the special purpose computer, one ormore operating systems, user applications, background services,background applications, etc.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language), XML (extensible markuplanguage), or JSON (JavaScript Object Notation), (ii) assembly code,(iii) object code generated from source code by a compiler, (iv) sourcecode for execution by an interpreter, (v) source code for compilationand execution by a just-in-time compiler, etc. As examples only, sourcecode may be written using syntax from languages including C, C++, C#,Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl,Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language 5threvision), Ada, ASP (Active Server Pages), PHP (PHP: HypertextPreprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, VisualBasic®, Lua, MATLAB, SIMULINK, and Python®.

What is claimed is:
 1. A computer system for transforming a userinterface according to data store mining, the computer systemcomprising: a data store configured to store a parameter related to auser and index event data of a plurality of events, wherein: each eventof the plurality of events corresponds to a physical object beingsupplied to the user identified by an identifier on behalf of an entity,and the data store is configured to store descriptive data for each of aplurality of identifiers; a data processing circuit configured to:identify a first set of identifiers from the plurality of identifiersbased on commonality among the descriptive data stored by the data storeacross the first set of identifiers; and train a machine learning modelfor the first set of identifiers based on event data stored by the datastore for the first set of identifiers from within a predeterminedepoch, wherein the machine learning model is trained using parallelprocessing of records from the data store, and wherein the parallelprocessing includes assigning analysis of the indexed event data of eachof a subset of the first set of identifiers to respective processorthreads for parallel execution on processing hardware; and an interfacecircuit configured to: receive an indication of a selected identifier ofthe plurality of identifiers; determine a first intake metric of theselected identifier using the machine learning model from the dataprocessing circuit; determine a second intake metric of the selectedidentifier and the parameter using the machine learning model from thedata processing circuit; and transform the user interface according tothe first intake metric and the second intake metric, wherein the firstintake metric represents an amount of resources expected to be receivedby the entity from the selected identifier during a second epochsubsequent to the predetermined epoch, and wherein the second intakemetric represents an amount of resources expected to be received by theentity from the selected identifier and the parameter during the secondepoch.
 2. The computer system of claim 1 further comprising a dataanalyst device configured to render the user interface and transmit amessage including the indication of the selected identifier.
 3. Thecomputer system of claim 2 wherein the data analyst device is configuredto: determine whether the first intake metric is greater than the secondintake metric; and in response to determining that the first intakemetric is greater than the second intake metric, calculate a differencebetween the first intake metric and the second intake metric.
 4. Thecomputer system of claim 3 wherein the data analyst device is configuredto, in response to determining that the first intake metric is greaterthan the second intake metric, render on the user interface at least oneof: the first intake metric; the second intake metric; and thecalculated difference between the first intake metric and the secondintake metric on the user interface.
 5. The computer system of claim 3wherein the data analyst device is configured to, in response to thedata analyst device determining that the first intake metric is notgreater than the second intake metric, render the second intake metricon the user interface.
 6. The computer system of claim 1 wherein: thedata processing circuit is configured to: identify the selectedidentifier from the first set of identifiers; and train the machinelearning model to predict an expected increase in the selectedidentifier during the second epoch; and the machine learning model istrained based on event data stored by the data store for the first setof identifiers from within the predetermined epoch.
 7. The computersystem of claim 6 wherein the interface circuit is configured todetermine the first intake metric for the selected identifier furtherbased on at least one of: a retention value that indicates a likelihoodof the selected identifier being associated with the entity for thesecond epoch; a population retention value that indicates a likelihoodof a population of identifiers encompassing the selected identifierbeing associated with the entity for the second epoch; and the expectedincrease in the selected identifier calculated by the data processingcircuit.
 8. The computer system of claim 6 wherein the interface circuitis configured to determine the second intake metric for the selectedidentifier based on: a retention value that indicates a likelihood ofthe selected identifier being associated with the entity for the secondepoch; a population retention value that indicates a likelihood of apopulation of identifiers encompassing the selected identifier beingassociated with the entity for the second epoch; and the expectedincrease in the selected identifier calculated by the data processingcircuit.
 9. A method for transforming a user interface according to datastore mining, comprising: storing, at a data store, a parameter relatedto a user; indexing, at the data store, a plurality of events, eachevent of the plurality of events corresponding to a physical objectbeing supplied to a user identified by an identifier on behalf of anentity, the data store being configured to store descriptive data foreach of a plurality of identifiers; identifying, at a data processingcircuit, a first set of identifiers from the plurality of identifiersbased on commonality among the descriptive data stored by the data storeacross the first set of identifiers; training, at the data processingcircuit, a machine learning model for the first set of identifiers basedon event data stored by the data store for the first set of identifiersfrom within a predetermined epoch; receiving, at an interface circuit,an indication of a selected identifier of the plurality of identifiers;determining, at the interface circuit, a first intake metric of theselected identifiers using the machine learning model from the dataprocessing circuit; determining, at the interface circuit, a secondintake metric of the selected identifier and the parameter using themachine learning model from the data processing circuit; andtransforming the user interface according to the first intake metric andthe second intake metric, wherein the machine learning model is trainedusing parallel processing of records from the data store, wherein theparallel processing includes assigning analysis of the indexed eventdata of each of a subset of the first set of identifiers to respectiveprocessor threads for parallel execution on processing hardware, whereinthe first intake metric represents an amount of resources expected to bereceived by the entity from the selected identifier during a secondepoch subsequent to the predetermined epoch, and wherein the secondintake metric represents an amount of resources expected to be receivedby the entity from the selected identifier and the parameter during thesecond epoch.
 10. The method of claim 9 further comprising: rendering,at a data analyst device, the user interface and transmitting a messagethat identifies a selected identifier of the plurality of identifiers;determining, at the data analyst device, that the first intake metric isgreater than the second intake metric; and calculating a differencebetween the first intake metric and the second intake metric.
 11. Themethod of claim 10 further comprising: rendering, on the user interfaceat the data analyst device, at least one of the first intake metric andthe second intake metric.
 12. The method of claim 10 further comprising:rendering, at the data analyst device, the calculated difference betweenthe first intake metric and the second intake metric.
 13. The method ofclaim 9 further comprising: rendering, at a data analyst device, theuser interface and transmitting a message that identifies a selectedidentifier of the plurality of identifiers; determining, at the dataanalyst device, that the first intake metric is not greater than thesecond intake metric; and rendering the second intake metric on the userinterface.
 14. The method of claim 9 further comprising: identifying, atthe data processing circuit, the selected identifier from the first setof identifiers; and training, at the data processing circuit, themachine learning model to predict an expected increase in the selectedidentifier during the second epoch.
 15. The method of claim 14 whereinthe machine learning model is trained based on event data stored by thedata store for the first set of identifiers from within thepredetermined epoch.
 16. The method of claim 15 wherein the first intakemetric is determined further based on the expected increase in theselected identifier calculated by the data processing circuit.
 17. Themethod of claim 16 wherein the first intake metric is determined furtherbased on: a retention value indicating a likelihood of the selectedidentifier being associated with the entity for the second epoch; and apopulation retention value indicating a likelihood of a population ofidentifiers encompassing the selected identifier being associated withthe entity for the second epoch.
 18. The method of claim 15 wherein thesecond intake metric is determined further based on: a retention valueindicating a likelihood of the selected identifier being associated withthe entity for the second epoch; a population retention value indicatinga likelihood of a population of identifiers encompassing the selectedidentifier being associated with the entity for the second epoch; andthe expected increase in the selected identifier calculated by the dataprocessing circuit.
 19. A non-transitory computer-readable mediumcomprising executable instructions for transforming a user interfaceaccording to data store mining, wherein the executable instructionsinclude: storing, in a data store, a parameter related to a user;indexing, in the data store, a plurality of events, wherein: each eventof the plurality of events corresponds to a physical object beingsupplied to a user identified by an identifier on behalf of an entity,and the data store is configured to store descriptive data for each of aplurality of identifiers; identifying a first set of identifiers fromthe plurality of identifiers based on commonality among the descriptivedata stored by the data store across the first set of identifiers;training a machine learning model for the first set of identifiers basedon event data stored by the data store for the first set of identifiersfrom within a predetermined epoch; receiving an indication of a selectedidentifier of the plurality of identifiers; determining a first intakemetric of the selected identifiers using the machine learning model;determining a second intake metric of the selected identifier and theparameter using the machine learning model; and transforming the userinterface according to the first intake metric and the second intakemetric.
 20. The non-transitory computer-readable medium of claim 19,wherein: the machine learning model is trained using parallel processingof records from the data store; the parallel processing includesassigning analysis of the indexed event data of each of a subset of thefirst set of identifiers to respective processor threads for parallelexecution on processing hardware; the first intake metric represents anamount of resources expected to be received by the entity from theselected identifier during a second epoch subsequent to thepredetermined epoch; and the second intake metric represents an amountof resources expected to be received by the entity from the selectedidentifier and the parameter during the second epoch.